Conventional object detection and tracking systems that include high resolution radar apparatus or LIDAR apparatus, for example, are generally implemented to track the position of objects in their field of view and estimate the velocity of detected objects. Such conventional object detection and tracking systems are generally configured for identifying and tracking target objects based on a point object model, which assumes that the system receives only one valid detection of a signal reflected from the target object. However, when a non-point object that has some substantial size and/or extended shape is located within a close range to a high resolution radar apparatus or LIDAR apparatus, the apparatus can receive multiple valid reflections from different parts of the non-point object.
The detection of valid reflections from a non-point object can be a source of error or inaccuracy in the output of an object detection and tracking system. For example, an automotive radar system may be configured to perform target velocity estimation by sensing the position of a target at multiple times. The point-object model used by conventional automotive radar systems assumes that each sensed position of the target represents the position of the entire target at each of the multiple times. However, if the target is a large object, such as a large truck, the conventional automotive radar system may receive a reflection from and sense the position of different parts of the large truck at different times. This can introduce substantial bias into a velocity estimation performed by a traditional detection and tracking system that uses a point-object model.